Autism spectrum disorder (ASD) is a highly heterogeneous group of neuro-developmental disorders. While strong familial evidence supports a substantial genetic contribution to the etiology of autism spectrum disorders (ASD), specific genetic abnormalities have been identified in only a small minority of all cases. The key limitation currently is the relative lack of families available for genetic analysis. In order to comprehensively delineate the heterogeneous genetic components of autism including the identification of rare and common variants, overall sample sizes an order of magnitude larger than those currently under study are critically needed. This will require rapid and scalable subject assessment paradigms that obviate clinic-based time- intensive behavioral phenotyping, which is a rate-limiting step. The project proposed here is to use the combined power of web based recruiting, web-implemented and validated phenotypic measurements, and distributed blood collection to establish a cost-effective recruitment of affected children with ASD. Based on our preliminary analysis where we assessed the accuracy of a web-based approach to autism phenotyping implemented within the Interactive Autism Network (IAN), we have set up a fully functional and automated protocol in IAN to recruit from already registered and newly registered families, consent them electronically and instruct them for blood collection at one of the 1,600 contracted blood draw sites near their home. This recruitment procedure is already initiated in collaboration with IAN and we expect that our plan of recruiting 400 simplex tetrad families per year will be readily met in the first three year of the funding period (specific aim 1). Using the collected sample set of 1200 tetrad families, we will perform highly sensitive targeted sequencing to search for rare risk variants and rare CNVs within a gene set that is enriched for genes associated with autism from previous studies (specific aim 2). Although the sample size proposed here is still not large enough to detect all rare risk variants, because of the stringent control using parents and the unaffected siblings, increased power to screen for novel rare mutations is achieved with statistically robust analytical strategy. Additionally, to convey the heterogeneous nature of ASD, we will implement exploratory subset analysis of both the gene-centric and the phenotypic-centric subgroups (specific aim 3). Throughout the recruitment process, we will carefully monitor the IAN population to ensure that there is no significant phenotype shifts observed. The variants found here will not only validate the previous genetic findings associated with autism but also result in identifying novel mutations and expanding the gene list. We would hope to eventually establish an environment in which all affected children's genetic makeup is known, relative to a comprehensive set of genetic risk factors, so that deeper phenotypic efforts can be applied to individuals with known genetic risk factors. All of the samples collected and the sequence data will be shared with other investigators, substantially increasing the sample size available for autism genetic studies.